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Study Objectives: Emergency department (ED) chief complaint data has several potential applications, including quality measurement, syndromic surveillance, operations, research, and education. However, there are no consistent methods to categorize ED chief complaints or evaluate their association with other ED outcomes, which has limited the utility of this type of data. To advance chief complaint data standardization, we report the initial results of a novel national ED chief complaint dataset under development in the Veteran Health Administration (VA). We identified common presenting ED chief complaints, characterized their associations with an ED discharge diagnosis of an emergent condition, and related admission rates. Method(s): This was a retrospective observational study of VA ED visits in FYs 2018-2020. A Natural Language Processing (NLP) program based on cTAKES, an Apache open source project, was applied to the original text of VA ED chief complaints. Results were mapped to Concept Unique Identifiers (CUI) in the Unified Medical Language System (UMLS). Multiple concepts could be identified from a chief complaint text entry. ED discharge diagnoses were defined by ICD-10 codes. Emergent diagnoses were selected based on a previously established list of codes for Emergency Care Sensitive Conditions (ECSCs), which are acute illnesses that require timely, quality emergency care to improve morbidity and mortality. Result(s): A total of 5,898,684 VA ED visits were identified with at least one ED chief complaint and a discharge diagnosis. 59% of visits had 1 chief complaint concept, 26% of visits had 2 concepts, 10% had 3 concepts, 4% had 4 concepts, and 1% had 5 concepts. The 10 most common chief complaints, associated rates of an ECSC discharge diagnosis, and respective admission rates for both ECSC and non-ECSC ED visits are depicted in Table 1. Among the most common chief complaints, dyspnea had a majority of ED visits with an ECSC diagnosis, likely due to the COVID-19 pandemic. Otherwise, rates of ECSC visits varied from 24% (coughing) to 5% (back pain). However, admission rates for ECSC visits ranged from 67% (abdominal pain) to 15% (pharmaceutical preparations). Conclusion(s): To our knowledge, this national ED chief complaint dataset is the largest in the country, and representative of a diverse patient population (ie by age, region, rurality). Initial work has highlighted areas for refinement of this dataset. Further work is ongoing to examine combinations of chief complaints to better predict ECSC diagnosis and admission rate given the variation in initial findings. Additionally, ongoing work to improve context detection and reduce mapping errors is underway, and will improve utility in multiple applications. [Formula presented] No, authors do not have interests to disclose Copyright © 2022
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Study Objectives: The COVID-19 pandemic has demonstrated that social determinants of health (SDOH) are profoundly linked to the spread and outcomes of COVID-19. However, the relationships between these SDOH and COVID-19 spatial outbreaks have yet to be determined. We conducted spatial analyses with geographic information systems (GIS) mapping of county-level SDOH and regional COVID-19 infection outbreaks to demonstrate the most impactful SDOH and to provide a pragmatic visual guide to prevent future outbreaks.
RESUMO
Study Objectives: Social determinants of health (SDOH) influence the health outcomes of COVID-19 patients;yet, little is known about how patients at risk of significant disease burden view this relationship. Our study sought to explore patient perceptions of the influence of SDOH on their COVID-19 infection experience and COVID-19 transmission within their communities. Methods: We conducted a qualitative study of patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020 through February 2021. All patients’ addresses across six counties served were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). Spatial autocorrelation analysis was performed to identify census area clusters of white, Black and Hispanic populations, based on the 2019 American Community Survey dataset. Patients were identified by a randomized computer-generated sampling method. After informed consent, patients participated in semi-structured phone interviews in English or Spanish based on patient preference by trained bilingual researchers. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge and diagnosis, disease experience, and the impact of SDOH. Results: The 10 patients interviewed from our COVID-19 hotspots were of equal distribution by sex, and predominantly Black (70%), ages 22-70 years (IQR 45-62 years), and presented to the ED for evaluation (70%). The respondents were more frequently publicly insured (50% medicaid/medicare;vs 30% uninsured;vs 20% private). The interviews demonstrated themes surrounding the experience and impact of COVID-19. The perceived risk of contracting COVID-19 and knowledge of how to prevent infection varied greatly among our sample, and could be in part explained by SDOH such as their occupation, living conditions and mode of transportation. The experiences of COVID-19 testing, diagnosis, isolation and medical treatment were most influenced by the timing of infection in relation to the study period. For example, in the early months of the pandemic, the knowledge of isolation requirements and available support systems seemed to have negatively impacted the ability to isolate and follow public health guidance, as well as the support mechanisms provided by employers during this period. Communication of infection status once diagnosed varied greatly, with some voicing feelings of shame, and others advocating for sharing of infection experiences to change community behaviors. Suggestions for how to improve the COVID-19 response included improving communication and enforcing public health guidelines, including raising awareness for vulnerable populations on topics like expected symptoms, financial support, increasing testing, and vaccination delivery. Conclusion: Further exploration of important themes and related SDOH that influenced how the participants experienced the COVID-19 pandemic will be necessary to decrease the negative impacts of SDOH in communities that are high-risk for COVID-19 spread.
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Study Objectives: Social determinants of health (SDOH) influence the health outcomes of COVID-19 patients;yet, little is known about how patients at risk of significant disease burden view this relationship. Our study sought to explore patient perceptions of the influence of SDOH on their COVID-19 infection experience and COVID-19 transmission within their communities. Methods: We conducted a qualitative study of patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020 through February 2021. All patients’ addresses across six counties served were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). Spatial autocorrelation analysis was performed to identify census area clusters of white, Black and Hispanic populations, based on the 2019 American Community Survey dataset. Patients were identified by a randomized computer-generated sampling method. Patients participated in semi-structured phone interviews in English or Spanish based on patient preference by trained bilingual researchers. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge and diagnosis, disease experience, and the impact of SDOH. Results: The 10 patients interviewed from our COVID-19 hotspots were of equal distribution by sex, and predominantly Black (70%), ages 22-70 years (IQR 45-62 years), and presented to the ED for evaluation (70%). The respondents were more frequently publicly insured (50% medicaid/medicare;vs 30% uninsured;vs 20% private). The interviews demonstrated themes surrounding the experience and impact of COVID-19. The perceived risk of contracting COVID-19 and knowledge of how to prevent infection varied greatly and could be in part explained by SDOH such as their occupation and living conditions. The experiences of COVID-19 testing, diagnosis, isolation and treatment were most influenced by the timing of infection in relation to the study period. Earlier in the pandemic, the knowledge of isolation requirements and available support systems seemed to have negatively impacted the ability to isolate and follow public health guidance, as well as the support mechanisms provided by employers during this period. Communication of infection status once diagnosed varied greatly, with some voicing feelings of shame, and others advocating for sharing of infection experiences to change community behaviors. Suggestions for how to improve the COVID-19 response included improving communication and enforcing public health guidelines, including raising awareness for vulnerable populations. Conclusion: Further exploration of important themes and related SDOH that influenced how the participants experienced the COVID-19 pandemic will be necessary to decrease the negative impacts of SDOH in communities that are high-risk for COVID-19 spread.
RESUMO
Study Objectives: The COVID-19 pandemic has demonstrated that social determinants of health (SDOH) are profoundly linked to the spread and outcomes of COVID-19. However, the relationships between these SDOH and COVID-19 spatial outbreaks have yet to be determined. We conducted spatial analyses with geographic information systems (GIS) mapping of county-level SDOH and regional COVID-19 infection outbreaks to demonstrate the most impactful SDOH and to provide a pragmatic visual guide to prevent future outbreaks. Methods: We analyzed the geospatial associations of COVID-19 infections and SDOH to identify areas of overlap. Our sample comprised all patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020-February 2021. Patients’ addresses were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). A set of 12 SDOH variables for each county were collected from the American Community Survey (ACS-5) and the Economic Research Service. Principal Component Analysis was applied to SDOH variables in order to reduce dimensions down to 3 geographical SDOH categories: Protective SDOH, High-Risk SDOH and Increased Vulnerability for Infection (Table 1). Using Multivariate Clustering Analysis (MCA), three clusters of census tracts were categorized according to SDOH indicators: decreased social disparities (DSD), equivocal social disparities (ESD) and increased social disparities (ISD) (Image A). Kruskal-Wallis and Dunn's Post-Hoc adjusted with Bonferroni were utilized to verify any difference in the proportion of patients residing in the different clusters (significance p<0.05). Results: A total of 13,733 patients were included in the study. The patients predominantly reside in Durham County (55.4%), are women (56.96%), and between 40 and 69 years old (41.9%). Further, patients are predominantly white (38.7%), non-Hispanic (79.63%), and single (49.6%). The concomitant analysis of KDE and MCA showed an overlap of COVID-19 hotspots with areas of ISD (Image B). The MCA revealed that there are 308 census tracts constituted by six counties, in which 40 form ISD clusters (vs. 109 ESD;vs. 159 DSD). In addition, ISD clusters have the highest rates of infection, with 179.8 patients per 10,000 (vs. 81.7 ESD;vs. 60.1 DSD). The ISD cluster was the most densely populated and was significantly more densely populated from the ESD and DSD clusters (p=0.01). Conclusion: In this sampling of COVID-19 patients, a disproportionate amount of patients come from areas with increased social disparities, suggesting further research and health policy will need to be directed towards addressing negative and vulnerability SDOH to curtail pandemic-related outbreaks. [Formula presented] [Formula presented]